{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version:2.3.1\n"
     ]
    }
   ],
   "source": [
    "print('TensorFlow version:{}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "# 面向对象路径管理工具\n",
    "import pathlib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = './dataset/2_class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建路径对象\n",
    "data_root = pathlib.Path(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('dataset/2_class')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset\\2_class\\airplane\n",
      "dataset\\2_class\\lake\n"
     ]
    }
   ],
   "source": [
    "# iterdir:对目录进行迭代\n",
    "for item in data_root.iterdir():\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取所有路径\n",
    "all_image_path = list(data_root.glob('*/*')) # 所有目录中的所有文件 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1400"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('dataset/2_class/airplane/airplane_001.jpg'),\n",
       " WindowsPath('dataset/2_class/airplane/airplane_002.jpg'),\n",
       " WindowsPath('dataset/2_class/airplane/airplane_003.jpg')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看前三张\n",
    "all_image_path[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('dataset/2_class/lake/lake_698.jpg'),\n",
       " WindowsPath('dataset/2_class/lake/lake_699.jpg'),\n",
       " WindowsPath('dataset/2_class/lake/lake_700.jpg')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最后三张\n",
    "all_image_path[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将WindowsPath格式的地址转为str类型的\n",
    "all_image_path = [str(path) for path in all_image_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dataset\\\\2_class\\\\airplane\\\\airplane_011.jpg',\n",
       " 'dataset\\\\2_class\\\\airplane\\\\airplane_012.jpg']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[10:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(all_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dataset\\\\2_class\\\\lake\\\\lake_129.jpg',\n",
       " 'dataset\\\\2_class\\\\airplane\\\\airplane_411.jpg']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[10:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1400"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_count = len(all_image_path)\n",
    "image_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['airplane', 'lake']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取分类名称\n",
    "label_names = sorted(item.name for item in data_root.glob('*/')) # 提取所有目录名\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为分类进行自动编码\n",
    "label_to_index =dict((name,index) for index,name in enumerate(label_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'airplane': 0, 'lake': 1}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_to_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lake'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出图片的上一级路径名为它的label\n",
    "pathlib.Path('dataset/2_class/lake/lake_700.jpg').parent.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有的图片与它对应的标签序号对应\n",
    "all_image_label = [label_to_index[pathlib.Path(p).parent.name] for p in all_image_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 0, 1, 0]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_label[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dataset\\\\2_class\\\\airplane\\\\airplane_520.jpg',\n",
       " 'dataset\\\\2_class\\\\lake\\\\lake_367.jpg',\n",
       " 'dataset\\\\2_class\\\\airplane\\\\airplane_461.jpg',\n",
       " 'dataset\\\\2_class\\\\lake\\\\lake_053.jpg',\n",
       " 'dataset\\\\2_class\\\\airplane\\\\airplane_374.jpg']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 引入显示图片\n",
    "import IPython.display as display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'airplane', 1: 'lake'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构造（序号：标签）字典\n",
    "index_to_label = dict((value,key) for key,value in label_to_index.items())\n",
    "index_to_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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ntcgJE7yk5+QfrWzN9mnAeFUVR2U8VBo85GsiMxopbPPcDHaqjoPdHrWgWz/2BYk8HylyD1HFX1iLcbhUfh6QNo0Q/u5FTFirsRW0ptHIo3bHfYyw++Pyqrf2LLZTqr/NsOOK0Y5DjkGmTfvEZT/EuKylO5cVZnlbtdXLSIApaMkYHUfWq1vqd6bb7FJHF+7YuuFw2fr6V0pLxr5e4ufUgA498Vzz23lajMwYnnOCKxvqdKehznna+56OvphQKaw15zkmX9BXrS+HdP8A+ech+rGhtEsE4W1dvxrTmQuY8k8jXHf5mfJ6fMK6HRDqNtdQWuoRELIy7GOM9eldnNp1tAUki08q2cbiayL3c+vWO9NuJVI/Oi9xOR1GwMOQeOmKmgUGCfAAyvPvUsMHm5ww4FRq3lpMpIORgYFCI6mPMoG5AR1yR+Vc7bkjxXamNnAEbEt610MwAZ+nQ4/SsPQRFJ4lhjcbgyOOa0w698ms/cOsEsg48xvzpVeRicyN+daSWcG0ZQZqQW0I6ItetyHlORlliw+Z2J/3qydcZ0gjIXcGJyc9BXUeREOqIPwrD8RFUtwqgcg1liIrlN6EryKejuXshuYkhsc1X8TW6vpE7MhkIwdo9KZo1wptDtdThz3rTvpUFk7FsgcnvXM17hr9s8hdYftDKseNo+bI5FRyWO1925AzckBTXo58S+HIhgxqXxziHv8AlXJ+KtVttTlzpmI3VNp+TGOev61wczudfIrFPSYY4dSt325O4AEVPqmptp2qgRWEUjNIwJPQYPpWXpXnQ6hAJZS6FuM9QRV/X2VbyTOQPP5+bGM0xI7C4ntLnS45odokbazLuBI9eM/09KpvHGCwMcUoK9JX2Y49u9R6f5R0hki2Z2jJ8vJ6/wB6lvb9FkQ2pQMI1Vi0W7BAwePwrSxXQrWxD+H7gCPyykwO3dnFU49Oubi3uhY7VYp8x4z+FAleKGZDIW805f5cDOamgSW4eKOJgpcHBJxyOakaMOXw3d2torzSzeWrZYF85P0pDpNw9mzLIwiT72W5OfStTU4p7eNDDJHKWA75xkZ5FZcp1CaIBAm4c8dqZRdi0XybaN1uIwuMhN+Scj/61GRZ6hDOg3EDoetZcUWorLuMm4Z+YY4p0v2icquWUZO7B6UroEejaP4xvYLVoLbRLm7AbJaM/KDjpV0eKNcZi0fhm4yf7zYqr8OPk027iZyziQNk+mAK7pR/KuaeIadhqmtzlV8S+J34Xw0R9Zcf0pza54sYYTw/Ep95x/hXWgcj/GlZckEc1DxMg9kjzhrHxfM5ZdMt0z288f41Cthr0fmy39knlIPmMbZI+nrXpw4PQ/lVTVwf7LuDjtnnpSjWbdinEYaU4yDTTS/w10mZWvTmNcf3q5y4QN4k04MAV81c10dzjYv1rm7ttviPTzz/AK5en1q4ks7aNEjmdQAo28AVnyA5b6mr55uG+lUZhwSKpkLcxbh1W5kRh0B57dq53TlceLbckFNqOeK3r2BJ7sIQS6MH2hucepHpWfo2D4pgMhG0o6nP0rTD6TCs/cOnEjnq5/Oje3qa0/3S8HaKdvhHQr+levzRPKtJ9DKJY+tY+usqWqF85ZsDg9a6wywj+NKwfEJjliQAqfoawxMo8mhth1LnPJp1mtJJTFdsoZiRtb1qZNavre1eKP8Ae7hj52NVJNMkiuZS+ZMtxnk0kFlcvc70Dt2CdsV5jm9j0OVXuRR3LMWMqlX5yo55qS2mjln2b484+YHqBVyLQrm5uHkaIRbDgBn60650WfS7eCeW1iVZicTZ6jpk96zauNJ3GN9mE9vHDICrP8m484z3q1r8Yhu59xYZw4K4znA9aqA2891HCZYI9rcMFJ5+tbl5p1nfTJNLO24IFPGc4GKEgsLYyqLCNRAQWiyzM4BP0HtVWzE8EryQShZSTtL4wPbnipLlFje3igQtHGhBkbrTrOWGCQvOjMhGCFGT1+h/lWiH0Kltf3E6XUN5GDM2W3jpxUtl5YMTuCyqfuipIbdIb25Ebs6OpIZs85571hzrNcSxwjcke7h1bBzSuNbHVajJZXs7yLB5O4AEZ74FLY3NrYIdtnDM3rIx9v8ACs3VtInnvbOOeZlYwIuVPyjkjJx39aRvCs8NxIrX0bRqN3+s684/pSBE001tBuaSGJd/IJOOnpVJlhUBkXbz1ByKivNKlFyYHn82BOFbOQcdKLpPsOnyOWXA2hRnvSshnf8Aw8vIZo7yNMcbWyB169a7xWXA4HSvC/DHiW+0yZ0TyCJRg5XkV3EPibVXUYkiX/gFcc6Mm9CvaxW56CrAEcVWkfE5Ab7rjIHbNcYuuaw3P2wD6Rik+3ao8hf7c6s2MlUXn9KlYeRP1iKO8ZSGyMnIIqnfoZ9LnjPUxn+VcqL/AFU8HUp/wC/4VFJc37Ag39wQeoyOf0qlQkncX1iJ0Bpx+6KaeopxHFdIyrdgFB654rm7pN3ifThk/wCtWuku+iH3rnbggeKdO/66CriKR2/luJWf1FZjyEORWyTxWHJjzfxqmjKOrMe9uLeLUSDBLNOwAHHyKPUn1rnp0lTUIgDh8nGK3L6WNPEXltGC7R5DFsevasmVl/tiH5s/PyT9alSaZtyprU1Y9O1Jo1YySDd0z2qaLSNRmbaHcH1Jrp3ubdQuZo8gDnd7UkeoWaOQ11Fk9MsP8al1Z9x+zj2Ock8PakM5mJ+hqCbQLqBRNK5IHJ9q6ibW9NiYg3sBOP74qjPqtje2MsMF1FLIAW2q+Tilzyb1DlS2ONS1iYl5FOc9BUF1Zi0aDdtAmPBWrM11DagtNKq5Pc1FrE8c9vZKGBV8DdnnkY4rSztchtGJf6qlurJFKN+7AaVRtFULO6vfEFzJBcbWtYQY1ZBjdycfzqleW9vayyw8+UjnIbJ71qeHWCaWzRPtxO+B+VTccHd6li38MabvIl+0FRls7sEcdKzmivLR5LmSGdUVsRNISQw7D61uPI+eGNRa1LOvh6OOFyC0hBwOKSkzRovaDcSXOiytKIRKQQVKrk59zSBreC1tZ1jLyB23bhgHuOTWDo1pd3bAebHlAfmZeTnsPyq/I/mWxsycjecLjnn3rVGQketrealKHjhjJ+UMvHtVe2ni87yndA+7AB+tRr4eurS6haZRGrfMv0oSxie/ZW4O7qRSGjqhpEnmuHmT5OWY8YB/+vWBqMktrfvEhSWMKDkAnJIB/rWndTQ3tr5GwI8fDOjc9KrEJ5PleXuP949aAMe5e4kK+RJ8ueSVIzVK5mka3KSQsXGDkDjJro57n7DAVkgWNcZBZcHsf8/Ws9rqK53sm0naAMDtSYzK06N/tifKyk9RXfWkZEYyDXGQX8cN/AkpTaM5IHI966yLWNORATeRAEcfN1oOWpe+hsJGxAwtTCJh2rKTxBYLEJPOyCQAAOaaPE+nl9vmN/3zU3M7SNrYSOBSGM4Oaxh4rsdhbEnHQbetR2fimG6lZJIniXsSOtNMXLK5323PNB5wKk/h6UzHIqDuuVLv7iLjvXN3A/4qrTh/01WulveFj+prmZiD4r0/n/lqvGK0iJnoB6GsNwTKfrW27BB8xx6ViM5DMf4T3pyIitTOuZIo7twbJZpHXaJMcr071x98A1zGrdGYggema6ye4jXUlf5tw6dgRjHpXLaiCuoR8Yy/T8ahGyHR6bFOgKQg4OMlzz+tMNlbgjMCD8KuW1nNcTJFEQGOTyTinzQPCXRimVbDY/z0oaKRTOnQ7UZoYRv5Hyjj61oadDDbXjhYYgVQklABkfhQumST2ktz5qBVJGxV/rVW3yjOFHJQ4FICaURSTg7BjnAIrO1hYkWAlR94YpLSa5e8KzgDaDgAdKTX2K2sYD7PmHOK7or9ycs/iMHVFSO+mJOAWz16VN4fVvsk4zkfaWIPYghaq6pve+kUhiSeeOtaeiI09o0du8W6NudzcZI4B9OhrhNKb1LxgOAVJPbkd6sXJaLw9Js2yNv+WJ+N3TvSiS8ufJgluIwiHG3bwuas6hIbHTZbZJEM7HcisOp/OkbM4qfVGsLSGJrYNOzbhkcAfWnWeqS3k376Py3ByrE9/Sq0tncSESz7i6DbnH9O1QgC3bfI5WNT82Bk4rVeRB0Ls8syeaxjZcHOMgiq0s7R3JbPO4lT7elNj1mG/kSGIhF4CMR1FOuLfduRiS+Rgin6gaNrqyw72SOISNncXXOexqGa7mkv1uGuxhW3qnG0e2Ky3tZILI+dNsmDcJszkfXpVSyuCtwPtWHiz8wBwcUMVjpPEGqpq8GxvLj+YEYHXr3NYtvJHA7EsCxGOOgrorLWPC08m2LRxIA3Cy3JOfyFTQaj4fsr26SXT7PzJGDxLIrOUz/CCOlTdXHY5QwW00hLSKJBwDmpEtraE/vH3EdutdTKZ9RQwwaNaRwq5IkRNjHPuT09qIvCjSFXnmEXokYyfzNS2mFjlL++srCNH8p3BPbjH0ql/wAJNaKci1Of9qtzxBbWsaCzRpXZTuJfaR6dhmucgtoblnQJtx6nqKaaQOxJ/wAJRHgMlttOeBjtTU8TzMzHy9qgHJC5qZ9KhCKwC7l6Y5p8dpGqFnCKAPm47Uc0UTzI+gCMDrmk64Oa5xtUuW5BcjPpVdtSusgYf86Q7G9fYCpgEkHr7Vx2qQ3S62lzGxjCDKOOcH1xV97u7lBVQzE/7VVLS3uNV1mDTZJEXzpFU4bLAfhTQFddV1gTK8twZlGfkYYBq9/wkN1HYtLLborA4HOc03VvDssHiG4sbSU+XCN2ZHIGP1Oa5y7nfBTcCAe1Oz6hZHSw6gkzJczK/wC8UE7WAC/gay9UuFudXjcHCA8HHvVfTo0lJJYbtmFy3T8KYwBnA644zSsM14JZ5pkhsVeWcnaNuAefc4qG9iuLCZzqUVxC3dmQMp/4EDis8GZMMs5DZ3K0fyshFdlpHi5HiFjq8YngK7fNYAn8afKhNtHKw6jEysIZJXUHkbeKtWt1FIS7RSxsAQMrXbXvhKy1eBJNPvDbLGuEWIZQD0x271y174WutLtXlvXmmkBwvkJmPbzyT1z07etLluJTMyVJnJFrCJJG6Cs6/sdUmt0iePZMjAuSePoKj/tWOKU+SZpCOyAkir9paazqsRuINMu/IQFnmbO1R75rS8krBZMyZNJvr++hdZFiEjBRGoBLMcAZ9BXpeveGNM8M6fplhawKk7qZLiUD5mbAGT+tU/AGkvqXiZLiSLdb2QDsSONx+7itL4l3hbxBHDGxBihGfYnms32GtzzvU7O/e43W8siKFPygcj3rObSdRkYztJI8qLnzJM/Lj3rZnvXjT95LtGOpOKqWevma+gt5JHNlG2+X0cD+D6GmoXHc5nzm0pgFuy8sh+fzeSf8KsaxenU7GC3dlVA+5dqDBHaumvvD/wDbtx/aaIqmUkopBwq5xjNZ2o+HhptrG8jqfmwOMhapRdrkc6vY5UW8ccg2rgKOPUVuaTOEuIfNywHJZulZdy5W5KLhkA67f61rWkJWG3mdAFZiu7nB6f8A1qlFXNm9mglgJjidmLsoCgZ+vPasNdBuJSY2TaHGMk9KszalbQ66LRpfm3bUwOBmtwlCCByTU3ZSRy9pbabo18iXMifuwQQOeRRc6rFc6lI8MLOuPkOOPauiksrWOFmER84A7igAxn8MmuTa7tImmtZXeMyKSk2M4b39KLjOx0zWpDp4O1cn9DUOr6ldS2LETtCP7ynGBWN4cDmdoJnAJUndu44rWv7Z5LR41IBNFkJHNCNpWDeYXPd92c1MLcB2ZfvDj8KgktpIpA5PlgHJHQE04XEcVyUc4IUk4Oe1SzKbuJd/aY7UmzOZc4BPaobKS+wVuWV2JJ5GMVdvJJ5reCOzLIoHzKg4b8KrfvQNsoCSg8cYNIdrI9M+8elKyc5AqWHhR061FdSrArSMSBnAwM81oWVlV2mO1th9cZrc0LT5rbxDpT7G8qdvMUiMcgZBY45Hb86k0XwZqmqsLy/c6bp+AcycSSD1x/D+Nd1Z3vh/TraOwsrqNmBEY5LMee5p3JexyfjG2uLXxWssNiJvtKKkckpIiRiepI9PQ1wniIjfFCI40MCCJimMFgTkj1r3TV/EGmaXIltf/MzgEKVyCK891XT/AAJrbmSG+/su7kbJKD5SfcdKEwOE0y8ntohELlY4wScdDn8BUDNuuXbeBk/eOcVt32ijQ7gCS7tb2JgTHLbngj3HY1hNnz8yRskTk7XI+VvbP9KrcDVutB13T4Y7iax+0WcuCt1bfOmD3OOnf8qznu7VEOJvNc8BIxkk12Xhga1f2xsNO1RobZRgqULKuT0H6/nXXW3guy0S+/tMW76nqBHzNIyxxqf7x44pXsK5yfgmDxSsrNbaWVsXA4uH29+vSu8/4R2+uY2OpaoIkI5jtlCj3+Y81Bf65fKhV9R0+wVRgpADO/58AUxNRe/tbVXuTKpGWY8bvei7YnYjuv8AhHPB6xiz02Ka5lJYOwBP1JNcjrnim81VWjeRYLXvDHwD/jTdX/tDxTqDf2CJpWWQR7pIdsca/U9TV7T/AIXRW7i78SaspY8lFbbii1tx3Oo8Hx2Ph3w6k13LHDNdHzXTOT7DFcz4o0HWvFuvtcaVD5FqURfOnG0n3A616BpFjoyxhrFYpWUYLk7m/Wptbjvm0yT+zLgQXA5BKg7vbnpU3HsedWnwj0q1RbnXtRaQqctl8LVa4u/BehXwWx0hL1gwDFlwqj29TWFql5eXM7Ge6aWQMQQ7ZIIrJS3kuZcb9hzjce1V6ha56zcX2keJNIj+ybbWWMEpEyhcEduK8y8WWs81pGIeGjkDEj2rY06D7PamESM+1j8zUzV1zYygllDcEqea6IxtE5ZP3zzU6NPPOS0jsGIIGMYx2rXlt5YIYbe4kKqvIQLxnufr0qW3soYtQgZDIyAjJLnr9Ku+IEH2qJgu0FelcnU7ErI5jVbO3/tJpyhJYg5x1wK0bXVCke0QNuUDBY9aj1kkyIsbOpZFwQM9qw72e+t7ORkGWYhVOOhpCuza/tm5a7kSW3AhB+8CTmsprdZbhmPR844zVa1+2xIpfncDuZ+ufaqKW+ou6tvZDkliG5P9KB6nUaRYxC8jiIc8gYHXFdNJGIpWUcjJHNcVZC8jj8tpgS3G7GW/SurjmQWUYLfOBzmhAZviEpK8cskfRQOMksR3rFkubCEia5Hl+cMDPX06V1MixTRgPgj3rC1XRIppUniAYgbSB2p2CxT03UdPgkJt7pnYHKhz6+lb2oz22oWaahC6uV4kCjnNZGnaFbzTBbiLywD97bk4rXt9JtrSU+U7eWGJAx1pWDlOxUgLn+KrFrePaTLPGkbSKSR5i7hn6VkrqO4/6r8zUgumI+4MfWqA17/Vb7UDvu7h3Lds4H5VDp0oj1O23DaFlU7j061m/aWzyBSw6J4g1tJZoo47axtxv8+Q4DkHoP8APahCsdV8SpF/tmAAg/uBjn3NeeMA8pOMY9q73xH4fuNdktdR0/UYJLhbdI3tJXCngc4zwMmuGdDCzK/Dg4I96pISIpd33VPB4xiuu8MWv2zRNV09rcXREAlitz3cHqPfFcvAm5txrY0fUpNK1CK7hJ3xnoD1HcUthm14a8RLpsiWqw28EDSDf8mCPXnrXQ6lr4vvD2pmOTBacRpj+7xj+tUvFHhtNcs18RaIu52TdPAnUkdSPcc5FcbJFqtnbiO6VYkkw6h2HJotcViw8yxq56nGck1065sdHRUkWOYoqKzLkDjk8VwkDia8jimljVN43kt0Gea6q8nsvEdxJZ2s0izW8TSRTIflyPvBh6Y7+1Owmh0Xii/tAIYZYoowQDsUKGANS+J9NkXytTiumktLhQfmfPlse3riqXhpdFhaRtZn+0M64VvL4T8f/rV12n6hpkOjXs9jYoBbISmTvz6HmkwOP0s6uzK+mWV47AYEigoDj3P411Gn+Nrq2lNrqtuHdOGKHkfX1rMH/CXaqiTM1usTdEa5Vcc+i1XbwbrFxIPOewVWbLBZ/mb2FA9Dpbvw7pHiKE6lYLH57BtvGFdvfHWvOZLC402/a3vrdoJ1bOGHBHsehr0TWbHWG0q0s7Gax0/yGyh87lsD+dc14uttfm0m0t5ry0UMfnmdlLkex/E00JMo2UyS/aCpBCyYOOe1R63G8mlOYkZjxgEYPWtmbR7bRLSyt7SI7TEGafdkysepyKqX+Xsycd1/mK6YLmicsnaVziFjmjkV2hYMpDYFWtY3ywwSSJsPOQeorpW8pVIZgPrXP63NbSoPnLbTg7TWFSCi9Dqpz5tDFuJGimhRl3iVeCF6D3p99a+XYmVQZGOP3ZXGOP8AGoLx0N3bxHzlDIApGMD/ABrZiYGHYxRmCdPU+1ZbGiRx80krLhLdcEckk5H0qvE9xD8gXKjpkV0V3A8NswkQISwYY7irbWtmXLE/MTnpSKSuY9lbXt4SqrbxnPDHdmthNJ1ML809pn1CMTVq1jign37gVIyAFNXTcxKepI+lTqa2RjPZagr7PtMXHBIj6U86VesD/wATAfhGKvPcJvLqGyTyMUG8Tsr/AI8U7sVomdJpF0pwdRkz/sqB/SrMOhPIgL6ldfQFf8KstdxyOCQQPUmnpeonA2j6tSux6GXb3BlfaMcelaSEkYrK01MzEjtWrjHStWc445A5qdry5e1S2a4mMCcCPdharKcNhhU2AaQFeZ3xkOwPTg07TdP0u+a4XUbua1n2Zt5wMqG7hx3HSo5zg4rS8MW5uNctgzIsStvl3Y5UdqpCaMm3h1G6vTZ2Onm5lBxujJ2kev0ro7T4e+LLqINILa0z/eOTWteeKb17+b7FN9ntQcRrGNuBVCfX9Tm6305GOfnNDYrM7vw3oWs6NpYsjqVs6hmYnyctknPrisXXPCXhOHU3udd1FjNKd5iD7AfoB0FWvBd1fw6df3d7DtiVRIAXy7YB/KvMNfefxjq9xezMlid2B5jFsr2x6VPUE7nT6mfhlpdu4FqZ3HACyEnPXrmvPbrxVFa3Ttpdv5EZTZt3k/KevvSz+EghUf2ikmeu1TxSL4MjcfNdP/3z/wDXq1YdrGNJ4lvWbKsi+4FWrTxnqtnaXVstxlLlNrAr0rYTwPZkc3En/fIqZfAmndTLL+lVeI9DlV8R6oPu3suM561ag8W6tCwYXZLKcgtziukj8EaZs5MpwT/FirA8GaQMZjkOPVqTkhWQap8TLjxF4djtJ7ZRdwEN5qtgvgcnHvxXLnxYZ5IzO0hCEYBbcAK6lfCmlRuGW3bP/XQ08eFdE5Jshnrksef1pJpAibSPFcGoW7RSXUccUR/dxn5QueuP0rfEkVzb4Vw6HuDmuNax0K2maP8AshCy9/MaiEF9QX7DNNaQDpCjZXjvzWiqpKxk6N5XLerW6TtIDuwnYHHT8ax3VVjWNM7V6k1qahfxJJJEYWmmY4DY4Xjr9c1lBiWbI+lZbmySQTW5Z7aVEVwfkck/d963rXSrb7TFI7SlU5Hz8Diubv7x7eziSOFmZ3J3L/CMVv6fJdy2cbva3LEgYwnWp5ZSegcyW5W8QhfspOckE81WYLwc9QDzWneWkksRElvKgbgBx1NY05u7BQLiBhkfJvXHAo5GtGVGS6F7cFdCSNu3nmhfIeTG0+xPNYsmpTS7VACYOOK6mz0uObTre4a7LGQkAAbcEetHINzRUMUDAHYuAfQUeVCSSI0U4/uisq/e7toWme4URhxGADhvyqHW9QFtqD29lceZbqqkFJCw5A70couY2lihA/1akH2AqWOOHABibC99nFUNG82Zw00xZXXCqT9DW9DACjklgFGRz3zUy0KuZ0VskbEqAM9cU8MN2OQ1TOuB9DTdoxnHNVczIlBUnnOTUobINMY4Y460whgvBwae4EcuWb1q1ZjAO0E8c1SBJcZrStQVbpjI60MCZU34UAZIyOe1PChDkBfx5rb0jTbSbw5q15MN88RURnH3MnP+NY8qOXKRLlicUrCTOumvBbeDhCuVknRix7gYrzfvxXY3dznSsOD/AKrHHPauMzgjHSiQU9SeMHzBmr6HiqKsAy1cifIxiiI5bluMjFTACqyNhCcVMj/KOKdiBQ3B5xzSgk9KRDlGGBnNOTHY0hjfX1o2A8nOacQCD60isQ2KLgjNfTop7s7lZQRnII5qaPR4beVZFd246HpU7KxuVGSCR1FSmNwCPN7Vnrc06GbeW1s4U/8ALYnkY4xWbcW9qbcmJv3yn5lPerrQbLoSbuOQuOaiuo7cRsMt559OmK1RJz93aySxrKrsFU4Iz0r0Pw15jaPblgBlBzu68muZ0mygvjLHOpZcYIziuu0i3itbJIolwqkr68ZNb0PiOevsVvEkEjWUZQMZPMAUJwc/WuT1LTbryFa6eYODj5zkAZ+prtfEi/8AEr8zJGxw2R1rzy5ludZkNrZyO21Gdi/UgdcYFOvpIeHd4mOzZ+RcElu31rZuNN1bTtOS7YeXC/3CsgJz7gcirH/CJmFraWOfnaHIkG8E9xXQ3Nhdf2eLd2jDr8xdYgAcc4xWNzc4D7HdXMMkqo8iJy7AZ25/z+lVbi1lgKGRCoddy59K6vU9amjsZbSwEUT3TESiPjdnGB+efzNY9zputTw+ZNCmyGHPDD7ooTuMm8PuGuEzOuAGXaevIrqo7hSrKGBGK5TQJJJb2MKAFRckBetdZDsLn5tp7YrKa1Ggnj7471VP1rbvtG1HT4917HbqG6BJgx/KsVuGqyUQSB/OGMbT1zQwwtOJyaY57cZ+tACRjcQMVu6Romq6o2LS0Z4+hlb5VH41hxe9akepagiZjv7hAq8BXIoEz0rSfCMtroF/plxcqZLrBLIvCf49K5LWPCGsWCs/7m5hHQxuA3/fJrZ0bXNE/wCELmSa6aWX7kwZix8w5xg9e1cQLqRxukllYehcmmiDfmDppzbl5EPII9q4lv4TXaT3ET2DIkoJaLGB64rjDjFTLYuG5Ipyy4q3GcVUUDKkCrS4wKIlSRaBJX2qaM5XFQqPkFTxEADNMgVeAT781J8u4elNX5mbFLgg0hjuNpGMUwgbwe9L0/GkJxj60CMq/vZbe7xHt+7kZFZ82p3j4/ebcf3BWjqEsUd2CyEkrnis9ruFfuxsTULc1WxqIdyRMQcsuaY9jatcveGf98qcxFSelLHJujgJTKYPWoLzVbC1g2S+Z55BHyqSK0IZVt53t53dPvc9K6XRtSi+yZuJ0Vw5OM9BXGJIXlbaThxwanghgjdneNnbccjcMHp2JrSE+R3InT51Y6fXvE2kvpcqLexs4wQPXmvP9E1Wyj1SaaW5eMbTtcH9PpW3M6Bx5OlRn0JK/wCNPRJAAG06FMc4yvFKdXmdyoUOVGtoV5pWsXDwtI8LeWzxs/RjjIHt3rbTToJJVZr11Dbif335DmuVS3WSUSvaRBxj5lPNabz3VpLG0NpIBj7zgMv/ANashtWJf7J091jjmtYJV2/fLMWB3EeuP0rP1OJdPtJYdNiWP5SHYN2IHr9KkXUL63uXcW8Gwj5yVB7Vnz3kjROZYgEY9Rg/1oVxmBo9xLb3a+XLtDFVbHfmuzt22zfcyP8A69efiRor8MpIAkHHtmu7Rsy53bRiiY4lp3yCScmqkw6nFTjrUUlNE2sVm6ioWGGzjmp2GWx61A3p1pgOTg1cgSJyqzgmI43heuO+KqRgCpVIDH1xSA6B7rw+tobW30iVIwQyyebhiw7msUvwQig8HlvQVGzfLw1VJ1LjG8r+PFNE2sdDHbTpD9pIXYY+w6cVzpPArpTrNn/ZZhMiAiLHXviuYBDAYGaJWHFMnj7VbQZHFUlypBq5E59BUxKkW04AzUykdcd6rqWwCcYqRXYHjGKoixYCgHPQk/0p4UM2M1CjliT6H+lScnHNSUKAOhH60FQSOOKXDA5zSqpbvQxFK8sI7i6QscfLinLoVuGRSoOetSzwSfaoXBG0Dnmr5t2yGL/jWdy0Y88CW/7lcYU9hXCavbzfa3aWUmPcSoY9K7nVN8d3tU5IPOTWReaMdSIYHaVHUrx+NaXJZh6fEUlyJg/yg/KchRW5bRGRJC+ciTuevAqnYBtOnmRYQ4kjMZJ7Z71rW0brnzABkBhxjjn+opscRHgAC47VI0JJ5zinyKQDUg+ZQT1qGdS2IghjWRgeQverV1ePcQpAs8ceAAeD+oqMxNIrRp95hgD1qjdazY2SqrmeO7XKkAggEe3fpTTbMaiLMmniKyEsd0PN3bdgj+/68k4qi9nbyWAkbUi85zm3WLjA9T+daFn4o8OjTnjnkm858knys4O3jH41zaXtu7B3EpUfexx+VUk+pkZF6y3GpBY4kgAwny+vc13ChflONw2dvpXn87Kb1inyqWyMnpXd22JbWF1Yf6sfxYxRNFF4ggkfjxUTjrUuN1NlGBQSVDw9Vn+8ast941Wfk0ATRip0UMxJ9KgjJI4qxEcMfpQwAoMcc1UnjKozHOADxV/cpOD1qGfaFJJ45oGjBcAQlhnOOOParEUn7sYPamur7TgMcg4+XrT4hmJOccUiwuJpEjXYec1W+2XZGAzfgKt3Kr5a5cj6VXxAo+aaQ/ShCaGfab/OC8hp6yXrdXYfVqAbPv5h/GpY5LDoYmP1amI1tH8wxP5hJIbua1RwazNKkiZJFhUqMg9c1p0Ihkmc4+lSxgdKh9KmjODmkxIp6rO8Bj2HGc5rObVLv/nqau6t8wRj64rDcmosaoneWWRknkZpCMgnGafdX17bvELPG2Th1Kg0yGcxwY2EjJLH04qlY+KLFLsrdQShd2FZRkn2rREsuWkInu5N6BgsTtg8Dpmn2aqWcrK7NhSwfkL16VHaXMcdzI4SQpJG20AkEA9M/rUmmMJrlbcwD96wAlz/AEptaCjuWZMqpoRvk9BSXxS1meBwSy8ZFVzeIBt2frWZ0qSRaZ2SN3U4KqTn8K4+K4sJXee8uWMkpO4BAcfQmunaZ3QnygFIx69a5HUNElsyJNrLE+ShI64q4Gc2mZEiIkx8tmaME7Se4qZLt0AXeQpPIIrVh8PPPHGz39pCJACA78jPY8deP1rDnsiLtlZ2Oxtvy5A+tXdEEsjiSYMueeatRXUsf3XYfjVVoXgcxyDDgZ61vaTolpqjW8c1yYskl/dR6elAmz//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lake\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 随机选择三张图片进行显示\n",
    "for n in range(3): \n",
    "    image_index = random.choice(range(len(all_image_path))) # 从all_image_path的长度中随机选择一个数字\n",
    "    display.display(display.Image(all_image_path[image_index])) # 显示图片\n",
    "    print(index_to_label[all_image_label[image_index]]) # 显示图片对应label\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dataset\\\\2_class\\\\airplane\\\\airplane_520.jpg'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_path = all_image_path[0]\n",
    "img_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=string, numpy=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xff\\xdb\\x00C\\x00\\x08\\x06\\x06\\x07\\x06\\x05\\x08\\x07\\x07\\x07\\t\\t\\x08\\n\\x0c\\x14\\r\\x0c\\x0b\\x0b\\x0c\\x19\\x12\\x13\\x0f\\x14\\x1d\\x1a\\x1f\\x1e\\x1d\\x1a\\x1c\\x1c $.\\' 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      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用tensorflow的方法读取图片(二进制形式\n",
    "img_raw = tf.io.read_file(img_path)\n",
    "img_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([256, 256, 3])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对图片数据进行解码\n",
    "img_tensor = tf.image.decode_image(img_raw)\n",
    "img_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tf.uint8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_tensor.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据类型转换：uint8->float\n",
    "img_tensor = tf.cast(img_tensor,tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化：归一化\n",
    "img_tensor = img_tensor/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.4862745 , 0.44313726, 0.43529412],\n",
       "        [0.48235294, 0.4392157 , 0.43137255],\n",
       "        [0.48235294, 0.4392157 , 0.43137255],\n",
       "        ...,\n",
       "        [0.49019608, 0.44705883, 0.4392157 ],\n",
       "        [0.49411765, 0.4509804 , 0.44313726],\n",
       "        [0.5058824 , 0.4627451 , 0.45490196]],\n",
       "\n",
       "       [[0.48235294, 0.4392157 , 0.43137255],\n",
       "        [0.47843137, 0.43529412, 0.42745098],\n",
       "        [0.47843137, 0.43529412, 0.42745098],\n",
       "        ...,\n",
       "        [0.49019608, 0.44705883, 0.4392157 ],\n",
       "        [0.49803922, 0.45490196, 0.44705883],\n",
       "        [0.5058824 , 0.4627451 , 0.45490196]],\n",
       "\n",
       "       [[0.48235294, 0.4392157 , 0.43137255],\n",
       "        [0.47843137, 0.43529412, 0.42745098],\n",
       "        [0.4745098 , 0.43137255, 0.42352942],\n",
       "        ...,\n",
       "        [0.49411765, 0.4509804 , 0.44313726],\n",
       "        [0.49803922, 0.45490196, 0.44705883],\n",
       "        [0.5019608 , 0.45882353, 0.4509804 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.45882353, 0.41568628, 0.43137255],\n",
       "        [0.45882353, 0.41568628, 0.43137255],\n",
       "        [0.4627451 , 0.41960785, 0.43529412],\n",
       "        ...,\n",
       "        [0.64705884, 0.6117647 , 0.5921569 ],\n",
       "        [0.67058825, 0.63529414, 0.6156863 ],\n",
       "        [0.6901961 , 0.654902  , 0.63529414]],\n",
       "\n",
       "       [[0.45490196, 0.4117647 , 0.42745098],\n",
       "        [0.45882353, 0.41568628, 0.43137255],\n",
       "        [0.45882353, 0.41568628, 0.43137255],\n",
       "        ...,\n",
       "        [0.65882355, 0.62352943, 0.6039216 ],\n",
       "        [0.6784314 , 0.6431373 , 0.62352943],\n",
       "        [0.69411767, 0.65882355, 0.6392157 ]],\n",
       "\n",
       "       [[0.45490196, 0.4117647 , 0.42745098],\n",
       "        [0.45490196, 0.4117647 , 0.42745098],\n",
       "        [0.45882353, 0.41568628, 0.43137255],\n",
       "        ...,\n",
       "        [0.6862745 , 0.6509804 , 0.6313726 ],\n",
       "        [0.69803923, 0.6627451 , 0.6431373 ],\n",
       "        [0.70980394, 0.6745098 , 0.654902  ]]], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_tensor.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_tensor.numpy().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25882354"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_tensor.numpy().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载和预处理函数：读取、解码、类型转换、标准化\n",
    "def load_preprosess_img(path):\n",
    "    # 使用tensorflow的方法读取图片(二进制形式\n",
    "    img_raw = tf.io.read_file(img_path)\n",
    "    # 对图片数据进行解码\n",
    "    img_tensor = tf.image.decode_jpeg(img_raw,channels=3)\n",
    "    # 改变图像大小的方法。此处无实际作用，只是告诉tensorflow图片的大小\n",
    "    img_tensor = tf.image.resize(img_tensor,[256,256])\n",
    "    # 数据类型转换：uint8->float\n",
    "    img_tensor = tf.cast(img_tensor,tf.float32)\n",
    "    # 数据标准化：归一化\n",
    "    img = img_tensor/255\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = all_image_path[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2bca37a5e48>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(load_preprosess_img(image_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: (), types: tf.string>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建地址dataset\n",
    "path_ds = tf.data.Dataset.from_tensor_slices(all_image_path)\n",
    "path_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 通过对每一个元素进行load_preprosess_img变化的得到一个新的dataset\n",
    "image_dataset = path_ds.map(load_preprosess_img) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MapDataset shapes: (256, 256, 3), types: tf.float32>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造标签dataset\n",
    "label_dataset = tf.data.Dataset.from_tensor_slices(all_image_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for label in label_dataset.take(10):\n",
    "    print(label.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ZipDataset shapes: ((256, 256, 3), ()), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.zip((image_dataset,label_dataset))\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(280, 1120)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 划分训练数据和测试数据\n",
    "test_count = int(image_count*0.2)\n",
    "train_count = image_count - test_count\n",
    "test_count,train_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 跳过test_count张数据，剩下的作为训练数据\n",
    "train_dataset = dataset.skip(test_count) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = dataset.take(test_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkipDataset shapes: ((256, 256, 3), ()), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将训练数据集进行乱序、重复和分批次\n",
    "train_dataset = train_dataset.repeat().shuffle(train_count).batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BatchDataset shapes: ((None, 256, 256, 3), (None,)), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = test_dataset.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "建立模型(增加BN层)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow' has no attribute 'kears'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-eb1d1f62e6f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSequential\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 顺序模型\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mConv2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minput_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkears\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBatchNormalization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 批标准化层\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mActivation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'relu'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 激活层\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'kears'"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential() # 顺序模型\n",
    "model.add(tf.keras.layers.Conv2D(64,(3,3),input_shape = (256,256,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.Conv2D(64,(3,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(128,(3,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.Conv2D(128,(3,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(256,(3,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.Conv2D(256,(3,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(512,(3,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.Conv2D(512,(3,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(1024,(3,3)))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.GlobalAveragePooling2D())\n",
    "model.add(tf.keras.layers.Dense(1024))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.Dense(256))\n",
    "model.add(tf.kears.layers.BatchNormalization()) # 批标准化层\n",
    "model.add(tf.keras.layers.Activation('relu')) # 激活层\n",
    "\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid')) # 二分类问题，逻辑回归问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss='binary_crossentropy',\n",
    "             metrics=['acc']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps_per_epoch = train_count//BATCH_SIZE\n",
    "validation_steps = test_count//BATCH_SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "35/35 [==============================] - 1312s 37s/step - loss: 0.6953 - acc: 0.5036 - val_loss: 0.6934 - val_acc: 0.4961\n",
      "Epoch 2/30\n",
      "35/35 [==============================] - 1037s 30s/step - loss: 0.6932 - acc: 0.5063 - val_loss: 0.6933 - val_acc: 0.4961\n",
      "Epoch 3/30\n",
      "35/35 [==============================] - 1045s 30s/step - loss: 0.6935 - acc: 0.4884 - val_loss: 0.6932 - val_acc: 0.4961\n",
      "Epoch 4/30\n",
      "35/35 [==============================] - 1018s 29s/step - loss: 0.6932 - acc: 0.4812 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 5/30\n",
      "35/35 [==============================] - 1024s 29s/step - loss: 0.6933 - acc: 0.4857 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 6/30\n",
      "35/35 [==============================] - 1024s 29s/step - loss: 0.6934 - acc: 0.4804 - val_loss: 0.6932 - val_acc: 0.4961\n",
      "Epoch 7/30\n",
      "35/35 [==============================] - 1014s 29s/step - loss: 0.6932 - acc: 0.4902 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 8/30\n",
      "35/35 [==============================] - 1019s 29s/step - loss: 0.6932 - acc: 0.4955 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 9/30\n",
      "35/35 [==============================] - 1016s 29s/step - loss: 0.6931 - acc: 0.5161 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 10/30\n",
      "35/35 [==============================] - 1018s 29s/step - loss: 0.6934 - acc: 0.4946 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 11/30\n",
      "35/35 [==============================] - 1017s 29s/step - loss: 0.6932 - acc: 0.5018 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 12/30\n",
      "35/35 [==============================] - 1012s 29s/step - loss: 0.6933 - acc: 0.4857 - val_loss: 0.6932 - val_acc: 0.4961\n",
      "Epoch 13/30\n",
      "35/35 [==============================] - 1018s 29s/step - loss: 0.6932 - acc: 0.5027 - val_loss: 0.6932 - val_acc: 0.4961\n",
      "Epoch 14/30\n",
      "35/35 [==============================] - 1024s 29s/step - loss: 0.6932 - acc: 0.4902 - val_loss: 0.6932 - val_acc: 0.4961\n",
      "Epoch 15/30\n",
      "35/35 [==============================] - 1032s 29s/step - loss: 0.6931 - acc: 0.5116 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 16/30\n",
      "35/35 [==============================] - 1393s 40s/step - loss: 0.6932 - acc: 0.4991 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 17/30\n",
      "35/35 [==============================] - 1624s 46s/step - loss: 0.6933 - acc: 0.4929 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 18/30\n",
      "35/35 [==============================] - 1817s 52s/step - loss: 0.6932 - acc: 0.4964 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 19/30\n",
      "35/35 [==============================] - 1130s 32s/step - loss: 0.6932 - acc: 0.5036 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 20/30\n",
      "35/35 [==============================] - 1404s 40s/step - loss: 0.6932 - acc: 0.5089 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 21/30\n",
      "35/35 [==============================] - 1074s 31s/step - loss: 0.6932 - acc: 0.5018 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 22/30\n",
      "35/35 [==============================] - 1030s 29s/step - loss: 0.6932 - acc: 0.4973 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 23/30\n",
      "35/35 [==============================] - 1023s 29s/step - loss: 0.6933 - acc: 0.4839 - val_loss: 0.6932 - val_acc: 0.4961\n",
      "Epoch 24/30\n",
      "35/35 [==============================] - 1014s 29s/step - loss: 0.6931 - acc: 0.4982 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 25/30\n",
      "35/35 [==============================] - 1020s 29s/step - loss: 0.6932 - acc: 0.4964 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 26/30\n",
      "35/35 [==============================] - 1093s 31s/step - loss: 0.6932 - acc: 0.4973 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 27/30\n",
      "35/35 [==============================] - 1187s 34s/step - loss: 0.6931 - acc: 0.5116 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 28/30\n",
      "35/35 [==============================] - 1541s 44s/step - loss: 0.6933 - acc: 0.4759 - val_loss: 0.6931 - val_acc: 0.5039\n",
      "Epoch 29/30\n",
      "35/35 [==============================] - 1551s 44s/step - loss: 0.6932 - acc: 0.5018 - val_loss: 0.6932 - val_acc: 0.4961\n",
      "Epoch 30/30\n",
      "35/35 [==============================] - 1596s 46s/step - loss: 0.6932 - acc: 0.4911 - val_loss: 0.6932 - val_acc: 0.4961\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_dataset,epochs = 30,steps_per_epoch = steps_per_epoch,validation_data=test_dataset,validation_steps = validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BatchDataset shapes: ((None, 256, 256, 3), (None,)), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2bca534b608>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch,history.history.get('acc'),'r',label='acc')\n",
    "plt.plot(history.epoch,history.history.get('val_acc'),'b--',label='val_acc')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2bca534b808>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch,history.history.get('loss'),'r',label='loss')\n",
    "plt.plot(history.epoch,history.history.get('val_loss'),'b--',label='val_loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
